**Format:**Texts

# Basic statistics for clinicians: 1. Hypothesis testing

**Language/s:**English

**Resource Link:**View the Text (PDF)

**Target Audience:**Further education

**Short Description:**

The statistical concepts of hypothesis testing and p values.

**Key Concepts addressed:**

- 2-8 Consider all of the relevant fair comparisons
- 2-17 Don’t confuse “statistical significance” with “importance”

## Details

**Abstract**

In the first of a series of four articles the authors explain the statistical concepts of hypothesis testing and p values. In many clinical trials investigators test a null hypothesis that there is no difference between a new treatment and a placebo or between two treatments. The result of a single experiment will almost always show some difference between the experimental and the control groups. Is the difference due to chance, or is it large enough to reject the null hypothesis and conclude that there is a true difference in treatment effects?

Statistical tests yield a p value: the probability that the experiment would show a difference as great or greater than that observed if the null hypothesis were true. By convention, p values of less than 0.05 are considered statistically significant, and investigators conclude that there is a real difference. However, the smaller the sample size, the greater the chance of erroneously concluding that the experimental treatment does not differ from the control – in statistical terms, the power of the test may be inadequate. Tests of several outcomes from one set of data may lead to an erroneous conclusion that an outcome is significant if the joint probability of the outcomes is not taken into account. Hypothesis testing has limitations, which will be discussed in the next article in the series.